Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition

Understand

Combining predictions from different models is a central problem in Bayesian inference and machine learning more broadly.

• Currently, these predictive distributions are almost exclusively combined using linear mixtures such as Bayesian model averaging, Bayesian stacking, and mixture of experts.
• Such linear mixtures impose idiosyncrasies that might be undesirable for some applications, such as multi-modality.
• While there exist alternative strategies (e.g.